Web-entity detection along with fair face classification [1]} and semi-supervised learning-based classification [2]} were also used for the hateful meme classification task.
Other noteworthy research includes using implicit models, e.g., topic modelling and multimodal cues, for detecting offensive analogy [3]} and hateful discrimination [4]} in memes. [5]} argued that online attention can be garnered immensely via fauxtography, which could eventually evolve towards turning into memes that potentially go viral. To support research on these topics, several datasets for offensiveness, hate speech, and harmfulness detection have been created [6]}, [7]}, [8]}, [9]}, [10]}, [11]}, [12]}.
